ArticleEuropean journal of epidemiology2021
Generalizing experimental results by leveraging knowledge of mechanisms.
Article in European journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Review
- Demystifying inconsistent two-sample mendelian randomization estimations using selection diagram.BMC medical research methodology · 2025Article
- When, why and how are estimated effects transported between populations? A scoping review of studies applying transportability methods.European journal of epidemiology · 2025Article
- Causally Sound Priors for Binary Experiments.Bayesian analysis · 2025Article
- Selection Bias in Health Research: Quantifying, Eliminating, or Exacerbating Health Disparities?Current epidemiology reports · 2024Article
- Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review.Statistical science : a review journal of the Institute of Mathematical Statistics · 2024Article
- Applicability of evidence from randomized controlled trials and systematic reviews to clinical practice: A conceptual review.Journal of rehabilitation medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
We show how experimental results can be generalized across diverse populations by leveraging knowledge of local mechanisms that produce the outcome of interest, only some of which may differ in the target domain. We use structural causal models and a refined version of selection diagrams to represent such knowledge, and to decide whether it entails the invariance of probabilities of causation across populations, which then enables generalization. We further provide: (i) bounds for the target effect when some of these conditions are violated; (ii) new identification results for probabilities of causation and the transported causal effect when trials from multiple source domains are available; as well as (iii) a Bayesian approach for estimating the transported causal effect from finite samples. We illustrate these methods both with simulated data and with a real example that transports the effects of Vitamin A supplementation on childhood mortality across different regions.
Indexed as
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.